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This is an interesting idea. Reprints of the most important AI papers, together with a discussion and sometimes even comments from the original authors. If your work isn't being cited much, you might want to consider what they all have in common...

Blog 3 of 4 on Bayesian methods in ML for RBC Borealis concerns Bayesian Neural Networks (i.e., Bayesian methods for NNs from a parameter-space perspective): rbcborealis.com/research-blogs… Parts 1 and 2 (linked in article) introduced Bayesian methods. Coming soon in part 4: NNGPs

I'm staggered to find that "Understanding Deep Learning" (udlbook.com) has been downloaded >400,000 times. Thanks all for your support (especially those who bought it). I'm staying on X for now, but new blogs will also be found on other platforms if you are not.

Extremely kind words from Justin Skycak about "Understanding Deep Learning". Justin himself has a host of useful resources for learning math for ML (see the links in his post) and an interesting summary of the science of learning. See justinmath.com/books/.

How I'd learn ML in 2025. youtu.be/_xIwjmCH6D4?si… (Me too) 😁

Learning or teaching from my book (udlbook.com)? I have now added the complete bibfile (which is accurate and took ages to make) and the LaTeX for all of the equations (helpful if you are making slides).

These blogs for RBC Borealis consider infinite-width networks from 4 viewpoints. We use gradient descent or a Bayesian approach, and focus on either the weights or output function. This leads to the Neural Tangent Kernel, Bayesian NNs and NNGPs. Enjoy! tinyurl.com/yfsts565

I'm starting a series of articles on ODEs and SDEs in ML for RBC Borealis. I'll describe ODEs and SDEs without assuming prior knowledge and present applications including neural ODEs, and diffusion models. Part I: rbcborealis.com/research-blogs…. Follow for parts II and III.

Here's the 2nd part of my series of articles on ODEs and SDEs in ML for RBC Borealis. rbcborealis.com/research-blogs… The article describes ODEs, vector ODEs, and PDEs and categorizes ODEs by how their solutions are related. It also describe conditions for an ODE to have a solution.

Here is part III of my series for RBC Borealis on ODEs and SDEs in machine learning. This article develops methods for solving first-order ODEs in closed form; we divide ODEs into different families and develop approaches to solve each family. rbcborealis.com/research-blogs…

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Exciting news! Travis LaCroix (@travislacroix.bsky.social) (who co-wrote the chapter on ethics in Understand Deep Learning) has a new book out "AI and Value Alignment". Recommended reading for anyone serious about ethics and AI. Details at: value-alignment.github.io Buy it here: broadviewpress.com/product/artifi…

👋 CALLING ALL STUDENTS! RBC Borealis is excited to announce our 2025 Fall Technical #Coop Program. This is your chance to work on real-world projects, build meaningful #solutions, and gain experience in #AI and #ML. View open roles & #apply by May 15: rbcborealis.com/technical-co-o…

If you are in the US and are considering buying a hard copy of Understanding Deep Learning, then it is currently available at 37% off on Amazon, for a very reasonable USD 56.99.

My friend Tyler Mills is looking for collaborators to work on the ARC-AGI competition. This benchmark is interesting and encourages creative approaches to AI. Tyler helped me with my book and would be a fun person to work with. Get in touch directly if you are interested.

Wow. Understanding Deep Learning has now been downloaded half a million times. Thank you so much everyone! I was overjoyed when it hit 100k so this is completely mindblowing. I'm so thrilled that people are finding it useful.